论文标题
机器学习与自动测试生成的集成：系统的映射研究
The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study
论文作者
论文摘要
上下文：机器学习（ML）可以实现有效的自动测试生成。 目的：我们表征了新兴研究，检查测试实践，研究人员目标，应用的ML技术，评估和挑战。 方法：我们对124个出版物的样本进行系统映射。 结果：ML生成系统，GUI，单位，性能和组合测试的输入或改善现有生成方法的性能。 ML还用于生成测试判决，基于属性的和预期的输出甲壳。经常基于神经网络和强化学习的监督学习通常是基于Q学习的 - 很普遍，并且某些出版物也采用了无监督或半监督的学习。使用传统的测试指标和与ML相关的指标（例如准确性）评估（半/非 - ）监督方法，而经常使用与奖励功能相关的测试指标来评估强化学习。 结论：工作到日期表现出巨大的希望，但是在培训数据，再培训，可伸缩性，评估复杂性，所采用的ML算法以及如何应用 - 基准和可复制性方面面临着公开挑战。我们的发现可以作为该领域研究人员的路线图和灵感。
Context: Machine learning (ML) may enable effective automated test generation. Objective: We characterize emerging research, examining testing practices, researcher goals, ML techniques applied, evaluation, and challenges. Methods: We perform a systematic mapping on a sample of 124 publications. Results: ML generates input for system, GUI, unit, performance, and combinatorial testing or improves the performance of existing generation methods. ML is also used to generate test verdicts, property-based, and expected output oracles. Supervised learning - often based on neural networks - and reinforcement learning - often based on Q-learning - are common, and some publications also employ unsupervised or semi-supervised learning. (Semi-/Un-)Supervised approaches are evaluated using both traditional testing metrics and ML-related metrics (e.g., accuracy), while reinforcement learning is often evaluated using testing metrics tied to the reward function. Conclusion: Work-to-date shows great promise, but there are open challenges regarding training data, retraining, scalability, evaluation complexity, ML algorithms employed - and how they are applied - benchmarks, and replicability. Our findings can serve as a roadmap and inspiration for researchers in this field.
